fig:globalallroc shows the ROC curves with their AUC's for the strong learner \(h\)  (i.e., \({\mathcal {SL}}\) : strong learner) and the MIL learner \(H\)  (i.e., \({\mathcal {WL}}\) : weak learner) on the validation split of the RSNA dataset as well as the CQ500 and CT-ICH datasets. AUC's are compared via a one-sided DeLong's test [1]}. fig:globalRSNAroc,fig:globalCQ500roc demonstrate that there is virtually no difference in performance between the strong and the weak learner on the validation split of the RSNA dataset and the CQ500 dataset, respectively. The strong learner achieves an AUC of \(0.961\) , whereas the weak learner obtains an AUC of \(0.960\)  (\(p = 0.636\) ) on the validation split of the RSNA dataset, and of \(0.901\)  and \(0.921\)  (\(p = 0.147\) ), respectively, on the CQ500 dataset. In fact, fig:globalCT-ICHroc suggests that the weak learner has significantly better generalization power on the CT-ICH dataset (AUC's of \(0.924\)  and \(0.954\) , respectively, \(p = 0.032\) ).
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